Bidirectional handshaking LSTM for remaining useful life prediction

Elsheikh, Ahmed H.; Yacout, Soumaya; Ouali, Mohamed-Salah

doi:10.1016/j.neucom.2018.09.076

Cited by 209 publications

(85 citation statements)

References 21 publications

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“…The MLE function is applied by multiplying each probability in Equation (6) after the most likely real outputs y are obtained from the inputs X 1 -X m and the model parameters θ 0 -θ m . Thus, Equation 7provides a likelihood estimation function based on m known samples and the loss function in Equation (8). Iterations of the gradient descent method with the learning rate (α) to find the value of each model parameter θ j are performed according to Equations (9) and (10).…”

Section: Lr Modelmentioning

confidence: 99%

“…Considering the entire history of a working asset until its failure would restrict the RUL model, as partial maintenance procedures will result in the asset exhibiting an improved health condition, and so only minimal repair is induced. The initial conditions of physical systems are usually unknown due to manufacturing deficiencies, the replacements of parts of the system, and imperfect maintenance [8].…”

Section: Introductionmentioning

confidence: 99%

“…This process requires information on degradation features and the relationship between the output of the reliability function and the estimated model parameters. Nevertheless, the LR method can be used to determine the RUL of a machine [5] or predict component failure [8]. Such RUL learning curves are based on the supervision of the machine status.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm

2019

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The prognosis and management of machine health statuses are emerging research topics. In this study, the performance degradation of a wafer-handling robot arm (WHRA) was predicted using the proposed machine-learning approach. This method considers the eccentric vertical and planar position deviations from a wafer mark using a charge-coupled device (CCD) camera. Synthesized position signals were defined using the square root of x- and y-axes deviations in the horizontal view and the square of the wafer mark diameter in the vertical view. A feature extraction method was used to determine the position status on the basis of these displacements and the area of a wafer mark in a CCD image. The root mean square error and mean, maximum, and minimum of the synthesized position signals were extracted through feature extraction and used for data mining by a general regression neural network (GRNN) and logistic regression (LR) models. The lifetime assessment by confidence value of the WHRA’s remaining useful life (RUL) by the genetic algorithm/GRNN exhibited nearly the same trend as that predicted through a run-to-failure LR model. The experimental results indicated that the proposed methodology can be used for proactive assessments of the RUL of WHRAs.

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Section: Lr Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm

2019

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“…Data-driven approaches immediately utilize historical sensor data as input and outputs lifetime information. Representative data-driven technology is machine learning, including neural network [8][9][10][11], support vector machine [12,13] and artificial intelligence approaches [14][15][16]. Such technologies can adopt diverse application scenarios, but heavily rely on the quality and size of data.…”

Section: Introductionmentioning

confidence: 99%

A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple Uncertainties

et al. 2020

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As crucial rotary components of high-speed trains, wheel treads in realistic operation environment usually suffer severe cyclic shocks, which damage the health status and ultimately cause safety risks. Timely and precise health prognosis based on vibration signals is an effective technology to mitigate such risks. In this work, a new parameter-related Wiener process model is proposed to capture multiple uncertainties existed in on-site prognosis of wheel treads. The proposed model establishes a quantitative relationship between degradation rate and variations, and integrates uncertainties via heterogeneity analysis of both criterions. A maximum-likelihood-based method is presented to initialize the unknown model parameters, followed by a recursive update algorithm with fully utilization of historical lifetime information. An investigation of real-world wheel tread signals demonstrates the superiority of the proposed model in accuracy improvement.

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“…Recently, many deep learning methods are utilized for RUL prediction (Zhao et al, 2019). Babu et al (2016) and Li et al (2018a) utilized deep convolution networks for RUL estimation, and Zheng et al (2017), Elsheikh et al (2019), and Huang et al (2019) utilized variations of deep recurrent networks to deal with this problem. These methods built the mapping from condition monitoring data to RUL values directly without prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

A method for degradation prediction based on Hidden semi-Markov models with mixture of Kernels

Yang

Zheng

2020

Computers in Industry

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Bidirectional handshaking LSTM for remaining useful life prediction

Cited by 209 publications

References 21 publications

Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm

Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm

A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple Uncertainties

A method for degradation prediction based on Hidden semi-Markov models with mixture of Kernels

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